Feminist Research in Crimino-Legal Studies: Reflections on 'Absolute Rubbish'
Bibliographic record
Abstract
This paper was initially written for an oral presentation at a seminar organised by the Legal Intersections Research Centre at the University of Wollongong. Translating it into a text form for a wider readership has not proved easy, given the lively, loud and gendered court-room dialogue upon which it draws is unable to be reproduced. The article commences with a brief sketch of some of the defining characteristics of research in feminist crimino-legal studies. This part of the paper assumes some familiarity with the epistemological debates that have hovered over social science research since the challenges of post-enlightenment philosophies to modernist knowledge claims. Then, reflecting on my own experiences as a witness subpoenaed before the Police Integrity Commission (PIC), the paper illustrates how the alignment between legal method with scientific positivism discredits feminist research in crimino-legal studies in much the same way as the legal process systematically disqualifies rape victims. I aim to demonstrate how this process of disqualification entails the asymmetrical arrangement of gendered bodies and spatiality in the hearing room, as well as the coercive exercise of a masculinist invalidation which operates through a micro-physics of power incompatible with the espoused judicial rhetoric of 'objectivity and neutrality'. The paper concludes with a salutary note about the future of feminist research in crimino-legal studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.111 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".